Multi-fault Diagnosis of Roller Bearings Using Support Vector Machines with an Improved Decision Strategy

M. M. Manjurul Islam, Sheraz A. Khan, Jong-Myon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)


This paper proposes an efficient fault diagnosis methodology based on an improved one-against-all multiclass support vector machine (OAA-MCSVM) for diagnosing faults inherent in rotating machinery. The methodology employs time and frequency domain techniques to extract features of diverse bearing defects. In addition, the proposed method introduces a new reliability measure (SVMReM) for individual SVMs in the multiclass framework. The SVMReM achieves optimum results irrespective of the test sample location by using a new decision strategy for the proposed OAA-MCSVM based method. Finally, each SVM is trained with optimized kernel parameters using a grid search technique to enhance the classification accuracy of the proposed method. Experimental results show that the proposed method is superior to conventional approaches, yielding an average classification accuracy of 97 % for five different rotational speed conditions, eight different fault types and two different crack sizes.
Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Number of pages13
ISBN (Electronic)978-3-319-22053-6
Publication statusPublished (in print/issue) - 13 Aug 2015

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Multi-fault diagnosis
  • Support vector machines
  • Dempster-Shafer (D-S) theory
  • Reliability measure
  • Decision rule


Dive into the research topics of 'Multi-fault Diagnosis of Roller Bearings Using Support Vector Machines with an Improved Decision Strategy'. Together they form a unique fingerprint.

Cite this